Real time vs Batch Serving

Real-time serving and batch serving are two distinct approaches to processing data in computing systems. Real-time serving focuses on processing data as it arrives, enabling immediate insights and actions, which is crucial for applications like fraud detection and live analytics. In contrast, batch serving processes data in large groups at scheduled intervals, allowing for more complex computations and optimizations, often used in scenarios like reporting and data warehousing. Each method has its advantages and trade-offs, making the choice between them dependent on specific use cases, resource availability, and performance requirements. Understanding these differences is essential for effective data management.

Is Real-Time Processing Worth It For Your Analytical Use Cases?

Towards Data Science

Real-time data pipelines provide a notable advantage over batch processing — data becomes available to consumers faster. In the traditional ETL, you would not be able to analyze events from today…

Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient

MachineLearningMastery.com

This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementat...

Batch Processing vs Stream Processing

Level Up Coding

When we encounter systems with large amounts of data, there are 2 main ways we can crunch that data to transform it into something useful for our organisation. One of those approaches is batch…

Streaming vs Batch: Two Philosophies of Data Processing

ByteByteGo Newsletter

When is the data complete enough to be moved to the compute stage?

How Modern Inference Servers Supercharge GPU Throughput with Batching

Towards AI

When deploying large language models (LLMs) or embedding models in production, throughput is king. A single request may feel instantaneous, but as traffic scales, the difference between serial executi...

The magical fusion between batch and streaming insights

Towards Data Science

Business needs data driven insights to reach stakeholders and customers as fast as possible and with clearly defined freshness requirements. The Lambda architecture (Marz, 2015) solves these requireme...

Stateful model serving: how we accelerate inference using ONNX Runtime

Towards Data Science

Stateless model serving is what one usually thinks about when using a machine-learned model in production. For instance, a web application handling live traffic can call out to a model server from…

Batch vs. Online Learning: Which Approach Fits Your Machine Learning Needs? (Part 2)

Python in Plain English

This blog post continues the discussion from my previous blog on batch learning, where I explained how models undergo training on large datasets in one go. In this post, we’ll explore online learning ...

LLM Serving: Why So Hard?

Towards AI

This is the second post in our series. Last time, we looked at the “parallelism salad” of DP, TP, PP, and EP. Today, I want to address the elephant in the room: Why does serving an LLM feel like syste...

Continuous Batching: How to Keep Your GPU Actually Busy

Towards AI

I was going over my LLM inference notes from Carnegie Mellon University today and I thought this is one of the important topics I should write about. So here we go 🙂 If you’ve been following along, w...

Batch And Streaming Demystified For Unification

Towards Data Science

Understand how batch can be considered a subset of streaming and why data engineering should simplify its usage significantly Continue reading on Towards Data Science

How to Design a Batch Processing?

Towards Data Science

We live in a world where every human interaction becomes an event in the system, whether it’s purchasing clothes online or in-store, scrolling social media, or taking an Uber. Unsurprisingly, all thes...